maeforecast.postnet: Economic Forecasting with High-Dimensional Data (Post...
In google-trends-v1/gtm: Functions for Economic Nowcasting with Google Trends Data

Usage Arguments Value Author(s) See Also

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maeforecast.postnet(data, w_size, window="recursive", pred, y.index=1,
            standardize=TRUE, h=0, t.select, t.update=F,
            alphas=c(0.2, 0.8, 0.02))

`data`	a data frame or a matrix; the first column should contain the time series variable for which the forecasts are to be made. Other columns should contain the covariates.
`w_size`	numeric, indicating the index where the forecasting should begin. If the first point forecast should be made at the 73th observation, for example, `w_size` should be set to be 72.
`window`	character, indicating the forecasting scheme to be applied. Options include `"recursive"`, `"rolling"`, and `"fixed"`.
`y.index`	numeric, indicating the column position of the time series for which the forecasts are made (Y). Defualt is `1`.
`t.select`	number of covariates to be included. If omitted, every covariate will be included. Otherwise, a regression between the dependant variable, its lag and each covariate will be run and a statistical test will be applied for the significance of the covariate's coefficient. The covariates will then be ranked based on their test statistics, and `t.select` number of them with the highest test statistics will be included in the machine learning algorithms. Note that the forecasting horizon `h` is considered.
`t.update`	logical, indicating wheter the preselection process should be repeated in evert iteration, if `t.select` is specified. Under the defualt `FALSE`, the preselection process will be implemented only with for the first window.
`standardize`	logical, indicating whether the data matrix should be scaled before the model is fitting, for the use of variable selection/shrinkage models. Default is `TRUE`.
`h`	forecasting horizon. Default is `0`.
`pred`	numeric, indicating the number of predicators being considered in the Adaptive Elastic Net model. Default is set to be the number of covariates. Note that if `t.select` is provided, the number of predictors cannot exceed `t.select`.
`standardize`	logical, indicating whether the data matrix should be scaled before the model is fitting, for the use of variable selection/shrinkage models. Default is `TRUE`.
`alphas`	vector of candidate `alpha`'s to use in `aenet`.

`Forecasts`	data matrix, containing the point forecasts, realized values, forecast errors, signs of the forecasts and realized values, and success in predicting the signs.
`MSE`	numeric, mean squred error of the point forecasts.
`SRatio`	numeric, success ratio of the point forecasts. Success is claimed when the point forecasts and realized values have the same sign.
`Data`	the data as used in the model.
`Model`	some specifics about the model used.
`Variables`	list, containing the predictors selected by the shrinkage model at every iteration.